Dataset Information
Tweeting back: predicting new cases of back pain with mass social media data.
Ontology highlight
ABSTRACT: Background
Back pain is a global health problem. Recent research has shown that risk factors that are proximal to the onset of back pain might be important targets for preventive interventions. Rapid communication through social media might be useful for delivering timely interventions that target proximal risk factors. Identifying individuals who are likely to discuss back pain on Twitter could provide useful information to guide online interventions.Methods
We used a case-crossover study design for a sample of 742?028 tweets about back pain to quantify the risks associated with a new tweet about back pain.Results
The odds of tweeting about back pain just after tweeting about selected physical, psychological, and general health factors were 1.83 (95% confidence interval [CI], 1.80-1.85), 1.85 (95% CI: 1.83-1.88), and 1.29 (95% CI, 1.27-1.30), respectively.Conclusion
These findings give directions for future research that could use social media for innovative public health interventions.
SUBMITTER: Lee H
PROVIDER: S-EPMC7839924 | biostudies-literature | 2016 May
REPOSITORIES: biostudies-literature
Publications
Tweeting back: predicting new cases of back pain with mass social media data.
Journal of the American Medical Informatics Association : JAMIA 20151211 3
<h4>Background</h4>Back pain is a global health problem. Recent research has shown that risk factors that are proximal to the onset of back pain might be important targets for preventive interventions. Rapid communication through social media might be useful for delivering timely interventions that target proximal risk factors. Identifying individuals who are likely to discuss back pain on Twitter could provide useful information to guide online interventions.<h4>Methods</h4>We used a case-cross ...[more]
